A Rao-Blackwellized Parts-Constellation Tracker

View/Open

Date

Author

Metadata

Abstract

We present a method for efficiently tracking objects represented
as constellations of parts by integrating out the
shape of the model. Parts-based models have been successfully
applied to object recognition and tracking. However,
the high dimensionality of such models present an obstacle
to traditional particle filtering approaches. We can efficiently
use parts-based models in a particle filter by applying
Rao-Blackwellization to integrate out continuous parameters
such as shape. This allows us to maintain multiple
hypotheses for the pose of an object without the need
to sample in the high-dimensional spaces in which partsbased
models live. We present experimental results for a
challenging biological tracking task.